





'''simple docstring'''


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[dict, list, tuple, torch.Tensor] )							->     List[Tuple[int, ...]]:

       """simple docstring"""


       UpperCAmelCase  =						[]
       if isinstance(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
              for v in tree.values():
                     shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE_ ) )
       elif isinstance(SCREAMING_SNAKE_CASE_			,	(list, tuple) ):
              for t in tree:
                     shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE_ ) )
       elif isinstance(SCREAMING_SNAKE_CASE_			,	torch.Tensor ):
              shapes.append(tree.shape )
       else:
              raise ValueError('''Not supported''' )

       return shapes
@torch.jit.ignore
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			Tuple[int, ...] )							->     Tuple[int, ...]:

       """simple docstring"""


       UpperCAmelCase  =						[]
       for d in reversed(SCREAMING_SNAKE_CASE_ ):
              idx.append(flat_idx % d )
              UpperCAmelCase  =						flat_idx // d

       return tuple(reversed(SCREAMING_SNAKE_CASE_ ) )
@torch.jit.ignore
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Sequence[int]			,	SCREAMING_SNAKE_CASE_ :			Sequence[int]			,	SCREAMING_SNAKE_CASE_ :			Sequence[int]			,	SCREAMING_SNAKE_CASE_ :			Optional[Sequence[bool]] = None			,	SCREAMING_SNAKE_CASE_ :			Optional[Sequence[bool]] = None			,	)							->     List[Tuple[slice, ...]]:

       """simple docstring"""


       def reduce_edge_list(SCREAMING_SNAKE_CASE_ :			List[bool] ) -> None:
              UpperCAmelCase  =						True
              for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
                     UpperCAmelCase  =						-1 * (i + 1)
                     l[reversed_idx] &= tally
                     UpperCAmelCase  =						l[reversed_idx]

       if start_edges is None:
              UpperCAmelCase  =						[s == 0 for s in start]
              reduce_edge_list(SCREAMING_SNAKE_CASE_ )
       if end_edges is None:
              UpperCAmelCase  =						[e == (d - 1) for e, d in zip(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )]
              reduce_edge_list(SCREAMING_SNAKE_CASE_ )

       # Base cases. Either start/end are empty and we're done, or the final,
       # one-dimensional tensor can be simply sliced
       if len(SCREAMING_SNAKE_CASE_ ) == 0:
              return [()]
       elif len(SCREAMING_SNAKE_CASE_ ) == 1:
              return [(slice(start[0]			,	end[0] + 1 ),)]

       UpperCAmelCase  =						[]
       UpperCAmelCase  =						[]

       # Dimensions common to start and end can be selected directly
       for s, e in zip(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
              if s == e:
                     path_list.append(slice(SCREAMING_SNAKE_CASE_			,	s + 1 ) )
              else:
                     break

       UpperCAmelCase  =						tuple(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						len(SCREAMING_SNAKE_CASE_ )

       # start == end, and we're done
       if divergence_idx == len(SCREAMING_SNAKE_CASE_ ):
              return [path]

       def upper() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              UpperCAmelCase  =						start[divergence_idx]
              return tuple(
                  path + (slice(SCREAMING_SNAKE_CASE_			,	sdi + 1 ),) + s
                  for s in _get_minimal_slice_set(
                      start[divergence_idx + 1 :]			,	[d - 1 for d in dims[divergence_idx + 1 :]]			,	dims[divergence_idx + 1 :]			,	start_edges=start_edges[divergence_idx + 1 :]			,	end_edges=[True for _ in end_edges[divergence_idx + 1 :]]			,	) )

       def lower() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              UpperCAmelCase  =						end[divergence_idx]
              return tuple(
                  path + (slice(SCREAMING_SNAKE_CASE_			,	edi + 1 ),) + s
                  for s in _get_minimal_slice_set(
                      [0 for _ in start[divergence_idx + 1 :]]			,	end[divergence_idx + 1 :]			,	dims[divergence_idx + 1 :]			,	start_edges=[True for _ in start_edges[divergence_idx + 1 :]]			,	end_edges=end_edges[divergence_idx + 1 :]			,	) )

       # If both start and end are at the edges of the subtree rooted at
       # divergence_idx, we can just select the whole subtree at once
       if start_edges[divergence_idx] and end_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx]			,	end[divergence_idx] + 1 ),) )
       # If just start is at the edge, we can grab almost all of the subtree,
       # treating only the ragged bottom edge as an edge case
       elif start_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx]			,	end[divergence_idx] ),) )
              slices.extend(lower() )
       # Analogous to the previous case, but the top is ragged this time
       elif end_edges[divergence_idx]:
              slices.extend(upper() )
              slices.append(path + (slice(start[divergence_idx] + 1			,	end[divergence_idx] + 1 ),) )
       # If both sides of the range are ragged, we need to handle both sides
       # separately. If there's contiguous meat in between them, we can index it
       # in one big chunk
       else:
              slices.extend(upper() )
              UpperCAmelCase  =						end[divergence_idx] - start[divergence_idx]
              if middle_ground > 1:
                     slices.append(path + (slice(start[divergence_idx] + 1			,	end[divergence_idx] ),) )
              slices.extend(lower() )

       return slices
@torch.jit.ignore
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			torch.Tensor			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			int )							->     torch.Tensor:

       """simple docstring"""


       UpperCAmelCase  =						t.shape[:no_batch_dims]
       UpperCAmelCase  =						list(_flat_idx_to_idx(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ) )
       # _get_minimal_slice_set is inclusive
       UpperCAmelCase  =						list(_flat_idx_to_idx(flat_end - 1			,	SCREAMING_SNAKE_CASE_ ) )

       # Get an ordered list of slices to perform
       UpperCAmelCase  =						_get_minimal_slice_set(
           SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	)

       UpperCAmelCase  =						[t[s] for s in slices]

       return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Callable			,	SCREAMING_SNAKE_CASE_ :			Dict[str, Any]			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			bool = False			,	SCREAMING_SNAKE_CASE_ :			Any = None			,	SCREAMING_SNAKE_CASE_ :			bool = False			,	)							->     Any:

       """simple docstring"""


       if not (len(SCREAMING_SNAKE_CASE_ ) > 0):
              raise ValueError('''Must provide at least one input''' )

       UpperCAmelCase  =						[shape[:no_batch_dims] for shape in _fetch_dims(SCREAMING_SNAKE_CASE_ )]
       UpperCAmelCase  =						tuple([max(SCREAMING_SNAKE_CASE_ ) for s in zip(*SCREAMING_SNAKE_CASE_ )] )

       def _prep_inputs(SCREAMING_SNAKE_CASE_ :			torch.Tensor ) -> torch.Tensor:
              if not low_mem:
                     if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
                            UpperCAmelCase  =						t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
                     UpperCAmelCase  =						t.reshape(-1			,	*t.shape[no_batch_dims:] )
              else:
                     UpperCAmelCase  =						t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
              return t

       UpperCAmelCase  =						tensor_tree_map(_prep_inputs			,	SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						None
       if _out is not None:
              UpperCAmelCase  =						tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : t.view([-1] + list(t.shape[no_batch_dims:] ) )			,	_out )

       UpperCAmelCase  =						1
       for d in orig_batch_dims:
              flat_batch_dim *= d

       UpperCAmelCase  =						flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

       def _select_chunk(SCREAMING_SNAKE_CASE_ :			torch.Tensor ) -> torch.Tensor:
              return t[i : i + chunk_size] if t.shape[0] != 1 else t

       UpperCAmelCase  =						0
       UpperCAmelCase  =						prepped_outputs
       for _ in range(SCREAMING_SNAKE_CASE_ ):
              # Chunk the input
              if not low_mem:
                     UpperCAmelCase  =						_select_chunk
              else:
                     UpperCAmelCase  =						partial(
                         _chunk_slice			,	flat_start=SCREAMING_SNAKE_CASE_			,	flat_end=min(SCREAMING_SNAKE_CASE_			,	i + chunk_size )			,	no_batch_dims=len(SCREAMING_SNAKE_CASE_ )			,	)

              UpperCAmelCase  =						tensor_tree_map(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )

              # Run the layer on the chunk
              UpperCAmelCase  =						layer(**SCREAMING_SNAKE_CASE_ )

              # Allocate space for the output
              if out is None:
                     UpperCAmelCase  =						tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : t.new_zeros((flat_batch_dim,) + t.shape[1:] )			,	SCREAMING_SNAKE_CASE_ )

              # Put the chunk in its pre-allocated space
              if isinstance(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):

                     def assign(SCREAMING_SNAKE_CASE_ :			dict			,	SCREAMING_SNAKE_CASE_ :			dict ) -> None:
                            for k, v in da.items():
                                   if isinstance(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
                                          assign(SCREAMING_SNAKE_CASE_			,	da[k] )
                                   else:
                                          if _add_into_out:
                                                 v[i : i + chunk_size] += da[k]
                                          else:
                                                 UpperCAmelCase  =						da[k]

                     assign(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
              elif isinstance(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
                     for xa, xa in zip(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
                            if _add_into_out:
                                   xa[i : i + chunk_size] += xa
                            else:
                                   UpperCAmelCase  =						xa
              elif isinstance(SCREAMING_SNAKE_CASE_			,	torch.Tensor ):
                     if _add_into_out:
                            out[i : i + chunk_size] += output_chunk
                     else:
                            UpperCAmelCase  =						output_chunk
              else:
                     raise ValueError('''Not supported''' )

              i += chunk_size

       UpperCAmelCase  =						tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : t.view(orig_batch_dims + t.shape[1:] )			,	SCREAMING_SNAKE_CASE_ )

       return out






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				int       ,     a__					:				int = 512       ,     ):
              UpperCAmelCase  =						max_chunk_size
              UpperCAmelCase  =						None
              UpperCAmelCase  =						None

       def 			__snake_case      ( self					:				Any       ,     a__					:				Callable       ,     a__					:				tuple       ,     a__					:				int						):
              logging.info('''Tuning chunk size...'''						)

              if min_chunk_size >= self.max_chunk_size:
                     return min_chunk_size

              UpperCAmelCase  =						[2**l for l in range(int(math.log(self.max_chunk_size       ,     2						)						) + 1						)]
              UpperCAmelCase  =						[c for c in candidates if c > min_chunk_size]
              UpperCAmelCase  =						[min_chunk_size] + candidates
              candidates[-1] += 4

              def test_chunk_size(a__					:				int						) -> bool:
                     try:
                            with torch.no_grad():
                                   fn(*a__       ,     chunk_size=a__						)
                            return True
                     except RuntimeError:
                            return False

              UpperCAmelCase  =						0
              UpperCAmelCase  =						len(a__						) - 1
              while i > min_viable_chunk_size_index:
                     UpperCAmelCase  =						test_chunk_size(candidates[i]						)
                     if not viable:
                            UpperCAmelCase  =						(min_viable_chunk_size_index + i) // 2
                     else:
                            UpperCAmelCase  =						i
                            UpperCAmelCase  =						(i + len(a__						) - 1) // 2

              return candidates[min_viable_chunk_size_index]

       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				Iterable       ,     a__					:				Iterable						):
              UpperCAmelCase  =						True
              for aa, aa in zip(a__       ,     a__						):
                     assert type(a__						) == type(a__						)
                     if isinstance(a__       ,     (list, tuple)						):
                            consistent &= self._compare_arg_caches(a__       ,     a__						)
                     elif isinstance(a__       ,     a__						):
                            UpperCAmelCase  =						[v for _, v in sorted(aa.items()       ,     key=lambda a__						: x[0]						)]
                            UpperCAmelCase  =						[v for _, v in sorted(aa.items()       ,     key=lambda a__						: x[0]						)]
                            consistent &= self._compare_arg_caches(a__       ,     a__						)
                     else:
                            consistent &= aa == aa

              return consistent



       def 			__snake_case      ( self					:				Any       ,     a__					:				Callable       ,     a__					:				tuple       ,     a__					:				int       ,     ):
              UpperCAmelCase  =						True
              UpperCAmelCase  =						tree_map(lambda a__						: a.shape if isinstance(a__       ,     torch.Tensor						) else a       ,     a__       ,     a__						)
              if self.cached_arg_data is not None:
                     # If args have changed shape/value, we need to re-tune
                     assert len(self.cached_arg_data						) == len(a__						)
                     UpperCAmelCase  =						self._compare_arg_caches(self.cached_arg_data       ,     a__						)
              else:
                     # Otherwise, we can reuse the precomputed value
                     UpperCAmelCase  =						False

              if not consistent:
                     UpperCAmelCase  =						self._determine_favorable_chunk_size(
                         a__       ,     a__       ,     a__       ,     )
                     UpperCAmelCase  =						arg_data

              assert self.cached_chunk_size is not None

              return self.cached_chunk_size





import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							return field(default_factory=lambda: default     ,				metadata=lowerCamelCase_		)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	list_field(
							    default=[]		,       metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }		,       )

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}				)

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8, 32, 1_28, 5_12]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Benchmark training of model'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Verbose memory tracing'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Trace memory line by line'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save result to a CSV file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save all print statements in a log file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to print environment information'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'env_info_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'log_{round(time()				)}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
							UpperCAmelCase_						:		int								=	field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Union[str, Any]:
														warnings.warn(
														    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
														    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
														    ''' to benchmark Transformer models.'''    ,						__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														return json.dumps(dataclasses.asdict(self						)    ,						indent=2						)




							@property
							def       snake_case			(					self						)		->       List[str]:
														if len(self.models						) <= 0:
																					raise ValueError(
																					    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																					    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
														return self.models






							@property
							def       snake_case			(					self						)		->       int:
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info('''Multiprocessing is currently not possible on TPU.'''						)
																					return False
														else:
																					return True






